Deep Learning with TensorFlow
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Course
Deep Learning with TensorFlow
Unlock the power of deep learning to make sense of unstructured data like images, sound, and text. Use TensorFlow to build intelligent models that solve real-world challenges.
Flexible Schedule
Beginner Level
Mentor Support
Duration
Deep learning has revolutionized the way machines learn from complex and unstructured data, such as images, text, and audio. Professionals skilled in deep learning are highly sought after, as these networks enable advanced AI applications that go beyond the capabilities of traditional shallow neural networks.
In this course, you will explore the fundamentals of TensorFlow, one of the leading libraries for implementing deep learning solutions. You will learn its core concepts, main functions, operations, and execution pipeline. Starting with a simple Hello World example, you will see how TensorFlow can be applied to curve fitting, regression, classification, and error minimization.
As you progress through the course, you will learn to use TensorFlow for backpropagation to tune weights and biases during neural network training. You will also explore different types of deep architectures, including convolutional networks, recurrent networks, and autoencoders, gaining practical knowledge for building advanced deep learning models.
For individuals aiming to build a career in deep learning, this course provides a strong foundation in TensorFlow and hands-on experience with real-world neural network architectures.
This course comprises five purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules. It is anticipated that you will complete the course in 3 hours. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
You will be able to:
- Master TensorFlow fundamentals.
- Build and train neural networks for real-world tasks like classification and regression.
- Implement backpropagation to optimize model performance.
- Design deep learning models.
- Apply deep learning to unstructured data such as text, images, and audio.
- Beginners in deep learning
- Anyone interested in using TensorFlow
- Learners working with images, text, sound, or unstructured data
- Basic understanding of neural networks
- Python programming experience
Course Outline
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
Youll explore core TensorFlow functions and operations, learn regression and classification techniques, and build models using CNNs, RNNs, and autoencoders. The course also covers unsupervised learning and error minimization, helping you apply deep learning concepts to real-world problems.
Yes, TensorFlow Deep Learning course is designed for learners who have basic Python knowledge and a general idea of neural networks. It starts with fundamentals and gradually moves into advanced topics, making it ideal for beginners.
Some Python experience is recommended. A general understanding of neural networks is helpful but not mandatory. The course provides step-by-step guidance to bridge knowledge gaps.
Youll work on real-world applications like image classification using CNNs, text or sequence modeling with RNNs, collaborative filtering, and dimensionality reduction with autoencoders. These projects ensure you practice what you learn.
Yes. Upon successful completion, youll receive an IBM certificate. This TensorFlow certification course helps validate your skills and adds credibility when applying for AI or machine learning roles.
By completing this program, youll build in-demand skills for roles such as Machine Learning Engineer, AI Engineer, and Data Scientist. Youll also gain hands-on experience, making you job-ready for careers with TensorFlow and Python in industries like healthcare, finance, and e-commerce.
Salaries vary by role and experience, but data shows competitive packages. For example, deep learning engineers and AI engineers with TensorFlow skills often earn high-paying jobs, with salaries ranging from entry-level positions to six-figure roles in advanced markets.
Industries such as healthcare, finance, retail, automotive, and technology are actively hiring professionals with deep learning expertise. Demand for TensorFlow developers is particularly strong in AI-driven companies.
Both frameworks are widely used in deep learning. TensorFlow is preferred for production and deployment due to its ecosystem, while PyTorch is often favored for research and experimentation. This course focuses on TensorFlow to prepare you for industry use cases and TensorFlow career opportunities.
Type of certificate
IBM Certificate
About this course
05 Modules
05 Skills
Includes
Discussion Space
10 Hands-On Labs
05 Quizzes
01 Final exam
Exercises to explore
Linear & logistic regression with TensorFlow
CNN with TensorFlow
Language Modelling with LSTM
Collaborative Filtering with RBM
Autoencoders
This course has been created by
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